Tabla de vida

A life table contains multiple columns: time intervals, number of individuals at study start, deaths during the interval, missing follow-ups, withdrawals alive, effective number exposed to risk, conditional probabilities of death and survival, and cumulative survival rates. Each column serves a specific purpose in tracking population mortality and survival patterns across defined time periods.

The effective number adjusts for participants withdrawn alive or lost to follow-up, assuming withdrawals occur uniformly throughout the interval. Typically, withdrawals are estimated to happen midway through the interval, reducing the denominator by half when calculating risks. This adjustment provides a more accurate representation of the population truly exposed to the risk being studied.

Conditional probability of death measures the likelihood of dying during a specific interval for those alive at the interval's start. Cumulative survival, conversely, measures the overall survival rate across all intervals from the beginning of the study. While conditional probability focuses on a single interval, cumulative survival tracks the combined effect of survival across multiple consecutive intervals.

Cohort life tables track a group of individuals born during the same period throughout their entire lifetimes, providing longitudinal survival data. Period life tables offer a snapshot of mortality rates for a population at a specific point in time, reflecting current age-specific death rates. Both approaches provide valuable insights into population survival patterns but differ in their temporal scope and data collection method.

Life tables are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions, estimate insurance risks, and study species survival patterns. They highlight differences in life expectancy between countries, assess intervention impacts on survival rates, and calculate life insurance premiums. Life tables provide essential data for forecasting future lifespans and comparing survival patterns across populations.

The number alive column indicates how many individuals survive to the start of each age interval. Starting from a hypothetical cohort, often 100,000, this column shows progressive decreases as individuals die or are lost to follow-up. It provides a clear picture of population attrition and serves as the denominator for calculating mortality and survival probabilities at each interval.

Life tables organize mortality and survival data systematically, allowing direct comparison of age-specific death rates and survival probabilities across populations or groups. By standardizing data into comparable intervals and calculating consistent metrics like mortality rates and cumulative survival, life tables enable researchers to identify population health differences and evaluate whether specific interventions or demographic factors influence survival outcomes.